Computational analyses for Inference of bacterial small RNA regulatory networks and integration with transcription factor driven regulatory networks
Mario Arrieta-Ortiz
November 24, 2019
- Load required libraries
library(ggplot2)
library(Hmisc)
library(multtest)
library(network)
library(igraph)1a. Examine the relation between the transcription of bacterial sRNAs and their regulatory activities - Part I (Fig 1)
#Name of sRNAs shown in Fig 1 A-B
fig1.sRNAs<-c("spf","ryhB")
#Load the output of the Inferelator run
load("../Inferelator_output_files/Ecoli_8sRNAs/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/params_and_input.RData")
#Extract expression response and design matrices
expression.response.matrix<-IN$final_response_matrix
expression.design.matrix<-IN$final_design_matrix
#This is the gold standard (prior) network
gs.matrix<-IN$gs.mat
#Matrix with regulators (TFs & sRNAs) activities
regulators.activities.matrix<-IN$tf.activities[[1]]
par(mfrow=c(1,2))
for(i in fig1.sRNAs)
{
sRNA.activities<-regulators.activities.matrix[grep(i,rownames(regulators.activities.matrix)),]
sRNA.transcriptional.profile<-expression.design.matrix[grep(i,rownames(expression.design.matrix)),]
sRNA.priors<-rownames(gs.matrix)[which(gs.matrix[,grep(i,colnames(gs.matrix))]!=0)]
sRNA.regulon.expression<-expression.response.matrix[sRNA.priors,]
mean.expression.sRNA.regulon<-colMeans(sRNA.regulon.expression)
plot(x=sRNA.transcriptional.profile,y=mean.expression.sRNA.regulon,col=rgb(1,0,0,1),lwd=1,xlab=paste(i,"transcription",sep=" "),ylab="Mean transcription of targets",cex=1.5, cex.lab=1.5,cex.axis=1.5)
#Perform linear regression
lr.sRNA.exp.regulon<-lm(mean.expression.sRNA.regulon ~ as.numeric(sRNA.transcriptional.profile))
abline(lr.sRNA.exp.regulon,col="darkred",lwd=3,lty=2)
plot(x=sRNA.activities,y=mean.expression.sRNA.regulon,col=rgb(1,0,0,1),lwd=1,xlab=paste(capitalize(i),"activity",sep=" "),ylab="",cex=1.5, cex.lab=1.5,cex.axis=1.5)
#Perform linear regression
lr.sRNA.activity.regulon<-lm(mean.expression.sRNA.regulon ~ as.numeric(sRNA.activities))
abline(lr.sRNA.activity.regulon,col="darkred",lwd=3,lty=2)
}
#Create similar figure for FnrS activity (its transcriptional profile is not present in the expression matrix)
sRNA.activities<-regulators.activities.matrix[grep("FnrS",rownames(regulators.activities.matrix)),]
sRNA.priors<-rownames(gs.matrix)[which(gs.matrix[,grep("FnrS",colnames(gs.matrix))]!=0)]
sRNA.regulon.expression<-expression.response.matrix[sRNA.priors,]
mean.expression.sRNA.regulon<-colMeans(sRNA.regulon.expression)
plot(x=sRNA.activities,y=mean.expression.sRNA.regulon,col=rgb(1,0,0,1),lwd=1,xlab="FnrS activity",ylab="Mean transcription of targets",cex=1.5, cex.lab=1.5,cex.axis=1.5, main="Fig 1D")
#Perform linear regression
lr.sRNA.activity.regulon<-lm(mean.expression.sRNA.regulon ~ as.numeric(sRNA.activities))
abline(lr.sRNA.activity.regulon,col="darkred",lwd=3,lty=2)
#Create Fig 1C
#Define correlation between sRNAs and their priors
sRNAs<-colnames(IN$priors.mat)[140:147]
correlation.sRNA.activities.targets.expression<-c()
correlation.sRNA.expression.targets.expression<-c()
for(t in sRNAs[-8])
{
sRNA.activities<-regulators.activities.matrix[t,]
sRNA.expression<-expression.design.matrix[t,]
sRNA.priors<-names(which(gs.matrix[,t]!=0))
for(g in sRNA.priors)
{
correlation.sRNA.activities.targets.expression<-c(correlation.sRNA.activities.targets.expression,
cor(sRNA.activities,expression.response.matrix[g,]))
correlation.sRNA.expression.targets.expression<-c(correlation.sRNA.expression.targets.expression,
cor(sRNA.expression,expression.response.matrix[g,]))
}
}
temporal.matrix<-rbind(cbind(correlation.sRNA.expression.targets.expression,rep("Expression",length(correlation.sRNA.expression.targets.expression))),cbind(correlation.sRNA.activities.targets.expression,rep("Activity",length(correlation.sRNA.activities.targets.expression))))
temporal.matrix<-as.data.frame(temporal.matrix)
colnames(temporal.matrix)<-c("PearsonCorrelation","Type")
temporal.matrix$PearsonCorrelation<-as.numeric(as.vector(temporal.matrix$PearsonCorrelation))
p <- ggplot(temporal.matrix, aes(x=Type, y=PearsonCorrelation,fill=Type)) + geom_violin()
p + stat_summary(fun.y=median, geom="point", size=2, color="purple") + ggtitle("Fig 1C")
1b. Examine the relation between the transcription of bacterial sRNAs and their regulatory activities - Part II (Fig S1)
#Create Fig S1A-E
other.ecoli.sRNAs<-c("cyaR","micA","gcvB","omrA","rybB")
#Load the output of the Inferelator
load("../Inferelator_output_files/Ecoli_8sRNAs/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/params_and_input.RData")
#Extract expression response and design matrices
expression.response.matrix<-IN$final_response_matrix
expression.design.matrix<-IN$final_design_matrix
gs.matrix<-IN$gs.mat
regulators.activities.matrix<-IN$tf.activities[[1]]
par(mfrow=c(1,2))
for(i in other.ecoli.sRNAs)
{
sRNA.activities<-regulators.activities.matrix[grep(i,rownames(regulators.activities.matrix)),]
sRNA.transcriptional.profile<-expression.design.matrix[grep(i,rownames(expression.design.matrix)),]
sRNA.priors<-rownames(gs.matrix)[which(gs.matrix[,grep(i,colnames(gs.matrix))]!=0)]
sRNA.regulon.expression<-expression.response.matrix[sRNA.priors,]
mean.expression.sRNA.regulon<-colMeans(sRNA.regulon.expression)
plot(x=sRNA.transcriptional.profile,y=mean.expression.sRNA.regulon,col=rgb(1,0,0,1),lwd=1,xlab=paste(i,"transcription",sep=" "),ylab="Mean transcription of targets",cex=1.5, cex.lab=1.5,cex.axis=1.5)
#Perform linear regression
lr.sRNA.exp.regulon<-lm(mean.expression.sRNA.regulon ~ as.numeric(sRNA.transcriptional.profile))
abline(lr.sRNA.exp.regulon,col="darkred",lwd=3,lty=2)
plot(x=sRNA.activities,y=mean.expression.sRNA.regulon,col=rgb(1,0,0,1),lwd=1,xlab=paste(capitalize(i),"activity",sep=" "),ylab="",cex=1.5, cex.lab=1.5,cex.axis=1.5)
#Perform linear regression
lr.sRNA.activity.regulon<-lm(mean.expression.sRNA.regulon ~ as.numeric(sRNA.activities))
abline(lr.sRNA.activity.regulon,col="darkred",lwd=3,lty=2)
}
#Create Fig S1F
#Load B. subtilis run
load("../Inferelator_output_files/FsrA_Bsubtilis/params_and_input.RData")
load("../Inferelator_output_files/FsrA_Bsubtilis/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/FsrA_Bsubtilis/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
#Extract expression response and design matrices
expression.response.matrix<-IN$final_response_matrix
expression.design.matrix<-IN$final_design_matrix
gs.matrix<-IN$gs.mat
regulators.activities.matrix<-IN$tf.activities[[1]]
FsrA<-"new_1483539_1483640"
sRNA.activities<-regulators.activities.matrix[FsrA,]
sRNA.transcriptional.profile<-expression.design.matrix[FsrA,]
sRNA.priors<-rownames(gs.matrix)[which(gs.matrix[,FsrA]!=0)]
sRNA.regulon.expression<-expression.response.matrix[sRNA.priors,]
mean.expression.sRNA.regulon<-colMeans(sRNA.regulon.expression)
plot(x=sRNA.transcriptional.profile,y=mean.expression.sRNA.regulon,col=rgb(1,0,0,1),lwd=1,xlab="fsrA transcription",ylab="Mean transcription of targets",cex=1.5, cex.lab=1.5,cex.axis=1.5, main="Fig S1F")
#Perform linear regression
lr.sRNA.exp.regulon<-lm(mean.expression.sRNA.regulon ~ as.numeric(sRNA.transcriptional.profile))
abline(lr.sRNA.exp.regulon,col="darkred",lwd=3,lty=2)plot(x=sRNA.activities,y=mean.expression.sRNA.regulon,col=rgb(1,0,0,1),lwd=1,xlab="FsrA activity",ylab="",cex=1.5, cex.lab=1.5,cex.axis=1.5)
#Perform linear regression
lr.sRNA.activity.regulon<-lm(mean.expression.sRNA.regulon ~ as.numeric(sRNA.activities))
abline(lr.sRNA.activity.regulon,col="darkred",lwd=3,lty=2)
#Create Fig S1F
#Load S. aureus run
load("../Inferelator_output_files/RsaE_Saureus/params_and_input.RData")
load("../Inferelator_output_files/RsaE_Saureus/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/RsaE_Saureus/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
#Extract expression response and design matrices
expression.response.matrix<-IN$final_response_matrix
expression.design.matrix<-IN$final_design_matrix
gs.matrix<-IN$gs.mat
regulators.activities.matrix<-IN$tf.activities[[1]]
RsaE<-"new_911368_911465_1"
sRNA.activities<-regulators.activities.matrix[RsaE,]
sRNA.transcriptional.profile<-expression.design.matrix[RsaE,]
sRNA.priors<-rownames(gs.matrix)[which(gs.matrix[,RsaE]!=0)]
sRNA.regulon.expression<-expression.response.matrix[sRNA.priors,]
mean.expression.sRNA.regulon<-colMeans(sRNA.regulon.expression)
plot(x=sRNA.transcriptional.profile,y=mean.expression.sRNA.regulon,col=rgb(1,0,0,1),lwd=1,xlab="rsaE transcription",ylab="Mean transcription of targets",cex=1.5, cex.lab=1.5,cex.axis=1.5, main="Fig S1G")
#Perform linear regression
lr.sRNA.exp.regulon<-lm(mean.expression.sRNA.regulon ~ as.numeric(sRNA.transcriptional.profile))
abline(lr.sRNA.exp.regulon,col="darkred",lwd=3,lty=2)plot(x=sRNA.activities,y=mean.expression.sRNA.regulon,col=rgb(1,0,0,1),lwd=1,xlab="RsaE activity",ylab="",cex=1.5, cex.lab=1.5,cex.axis=1.5)
#Perform linear regression
lr.sRNA.activity.regulon<-lm(mean.expression.sRNA.regulon ~ as.numeric(sRNA.activities))
abline(lr.sRNA.activity.regulon,col="darkred",lwd=3,lty=2)
2a. Evaluate performance of the Inferelator and CLR with and without sRNA activities (Fig 3A)
#Plot performance of the Inferelator with sRNA activities
#Load the output files of the Inferelator run for E. coli
load("../Inferelator_output_files/Ecoli_8sRNAs/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/params_and_input.RData")
#Normalize confidence score matrix
source("../Miscellaneous_scripts/create_final_networks.R")[1] 0.3668516#Save the names of sRNAs to be considered in this analysis (FnrS is not included)
sRNAs<-colnames(IN$priors.mat)[140:146]
#Create matrix with normalized confidence score for selected sRNAs
normalized.confidence.matrix.sRNAs<-normalized.confidence.matrix[,sRNAs]
#Convert sRNA priors to zeroes in the normalized confidence score matrix
normalized.confidence.matrix.sRNAs[which(IN$priors.mat[,sRNAs]!=0)]<-0
#Run script to read all candidate sRNA targets supported by experimental data and genomic location (operons)
source("../Miscellaneous_scripts/sRNA_candidate_targets_compilation.R")
#Create vector to save number of supported priors at different ranking positions per sRNA
performance.inferelator.sRNA.activity<-c()
for(y in 1:100)
{
total.supported.predictions<-0
for(s in colnames(normalized.confidence.matrix.sRNAs))
{
candidate.targets<-unique(expanded.sRNA.gs[which(expanded.sRNA.gs[,1]== s),2])
top.targets<-rownames(normalized.confidence.matrix.sRNAs)[order(normalized.confidence.matrix.sRNAs[,s],decreasing=T)[1:y]]
#Keep only the loci tags
top.targets<-translate.names(top.targets)
total.supported.predictions <- total.supported.predictions + length(intersect(candidate.targets,top.targets))
}
performance.inferelator.sRNA.activity<-c(performance.inferelator.sRNA.activity,total.supported.predictions)
}
plot(x=1:100, y=performance.inferelator.sRNA.activity,ylim=c(0,72),xlab="Targets per sRNA",ylab="Supported predictions",col="dark green",type="lines",lwd=2,cex=1.5,cex.lab=1.5,cex.axis=1.5)
#Plot performance of CLR without sRNA activities (sRNA expression instead) using script in the folder of the Inferelator code
source("../Inferelator_2015.08.05/R_scripts/mi_and_clr.R")
#Remove FnrS from the set of potential regulators
regulators.names<-IN$tf.names[-147]
X<-IN$final_design_matrix[regulators.names,]
#This is the same IN$final_response_matrix because we did not use a metadata file (for time series)
Y<-IN$final_response_matrix_halftau
#Calculate mutual information (MI) as done in the Inferelator
Ms <- mi(t(Y), t(X), nbins=PARS$mi.bins, cpu.n=PARS$cores)
diag(Ms) <- 0
Ms_bg <- mi(t(X), t(X), nbins=PARS$mi.bins, cpu.n=PARS$cores)
diag(Ms_bg) <- 0
#Calculate CLR
clr.matrix = mixedCLR(Ms_bg,Ms)
dimnames(clr.matrix) <- list(rownames(Y), rownames(X))
#Names of sRNA to be considered in this analysis (FnrS is not included)
sRNAs<-colnames(IN$priors.mat)[140:146]
#Create matrix with normalized CLR scores for selected sRNAs
clr.matrix.sRNAs<-clr.matrix[,sRNAs]
performance.clr.sRNA.exp<-c()
for(y in 1:100)
{
total.supported.predictions<-0
for(s in colnames(clr.matrix.sRNAs))
{
candidate.targets<-unique(expanded.sRNA.gs[which(expanded.sRNA.gs[,1]== s),2])
top.targets<-rownames(clr.matrix.sRNAs)[order(clr.matrix.sRNAs[,s],decreasing=T)[1:y]]
top.targets<-translate.names(top.targets)
total.supported.predictions <- total.supported.predictions + length(intersect(candidate.targets,top.targets))
}
performance.clr.sRNA.exp<-c(performance.clr.sRNA.exp,total.supported.predictions)
}
lines(x=1:100, y=performance.clr.sRNA.exp,col="orange",lwd=2)#Plot performance of CLR with sRNA activities
X<-IN$tf.activities[[1]]
Y<-IN$final_response_matrix_halftau
#Calculate mutual information (MI) as done in the Inferelator
Ms <- mi(t(Y), t(X), nbins=PARS$mi.bins, cpu.n=PARS$cores)
diag(Ms) <- 0
Ms_bg <- mi(t(X), t(X), nbins=PARS$mi.bins, cpu.n=PARS$cores)
diag(Ms_bg) <- 0
#Calculate CLR
clr.matrix = mixedCLR(Ms_bg,Ms)
dimnames(clr.matrix) <- list(rownames(Y), rownames(X))
#Create matrix with normalized clr scores for selected sRNAs
clr.matrix.sRNAs<-clr.matrix[,sRNAs]
#Convert sRNA priors to zeroes in the CLR matrix
clr.matrix.sRNAs[which(IN$priors.mat[,sRNAs]!=0)]<-0
performance.clr.sRNA.act<-c()
for(y in 1:100)
{
total.supported.predictions<-0
for(s in colnames(clr.matrix.sRNAs))
{
candidate.targets<-unique(expanded.sRNA.gs[which(expanded.sRNA.gs[,1]== s),2])
top.targets<-rownames(clr.matrix.sRNAs)[order(clr.matrix.sRNAs[,s],decreasing=T)[1:y]]
top.targets<-translate.names(top.targets)
total.supported.predictions <- total.supported.predictions + length(intersect(candidate.targets,top.targets))
}
performance.clr.sRNA.act<-c(performance.clr.sRNA.act,total.supported.predictions)
}
lines(x=1:100, y=performance.clr.sRNA.act,col="blue",lwd=2)
#Plot performance of the Inferelator without sRNA activities (no TF or sRNA priors were specified)
#Load Inferelator output
load("../Inferelator_output_files/Ecoli_without_regulators_priors/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_0.RData")
load("../Inferelator_output_files/Ecoli_without_regulators_priors/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_0.RData")
load("../Inferelator_output_files/Ecoli_without_regulators_priors/params_and_input.RData")
#Create matrix with normalized confidence score for selected sRNAs
normalized.confidence.matrix<-normalization(as.matrix(comb.confs))
normalized.confidence.matrix.sRNAs<-normalized.confidence.matrix[,sRNAs]
performance.inferelator.sRNA.exp<-c()
for(y in 1:100)
{
total.supported.predictions<-0
for(s in colnames(normalized.confidence.matrix.sRNAs))
{
candidate.targets<-unique(expanded.sRNA.gs[which(expanded.sRNA.gs[,1]== s),2])
top.targets<-rownames(normalized.confidence.matrix.sRNAs)[order(normalized.confidence.matrix.sRNAs[,s],decreasing=T)[1:y]]
top.targets<-translate.names(top.targets)
total.supported.predictions <- total.supported.predictions + length(intersect(candidate.targets,top.targets))
}
performance.inferelator.sRNA.exp<-c(performance.inferelator.sRNA.exp,total.supported.predictions)
}
lines(x=1:100, y=performance.inferelator.sRNA.exp,col="purple",lwd=2)#Plot performance of the Inferelator with sRNA activities and shuffled sRNA priors
performance.inferelator.shuffled.sRNA.priors<-c()
for(r in 1:10)
{
shuffled.path<-paste("../Inferelator_output_files/Ecoli_shuffled_sRNA_priors/Ecoli_shuffled_sRNA_priors",r,"/",sep="")
#Load Inferelator output files
load(paste(shuffled.path,"combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData",sep=""))
load(paste(shuffled.path,"params_and_input.RData",sep=""))
load(paste(shuffled.path,"betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData",sep=""))
#Create matrix with normalized confidence score for selected sRNAs
normalized.confidence.matrix.shuffled.priors<-normalization(as.matrix(comb.confs))
normalized.confidence.matrix.shuffled.priors.sRNAs<-normalized.confidence.matrix.shuffled.priors[,sRNAs]
#Convert sRNA priors to zeroes in the normalized confidence score matrix
normalized.confidence.matrix.shuffled.priors.sRNAs[which(IN$priors.mat[,sRNAs]!=0)]<-0
temporal.performance<-c()
for(y in 1:100)
{
total.supported.predictions<-0
for(s in colnames(clr.matrix.sRNAs))
{
candidate.targets<-unique(expanded.sRNA.gs[which(expanded.sRNA.gs[,1]== s),2])
top.targets<-rownames(normalized.confidence.matrix.shuffled.priors.sRNAs)[order(normalized.confidence.matrix.shuffled.priors.sRNAs[,s],decreasing=T)[1:y]]
top.targets<-translate.names(top.targets)
total.supported.predictions <- total.supported.predictions + length(intersect(candidate.targets,top.targets))
}
temporal.performance<-c(temporal.performance,total.supported.predictions)
}
performance.inferelator.shuffled.sRNA.priors<-rbind(performance.inferelator.shuffled.sRNA.priors,temporal.performance)
}
lines(x=1:100, y=colMeans(performance.inferelator.shuffled.sRNA.priors),col="dark grey",lwd=2)
legend("topleft",inset=0.03,box.lty=0.1,box.lwd=0.2,c("BBSR.SRA","BBSR.SRA.Shuffled","BBSR","CLR.SRA","CLR"),col=c("dark green","dark grey","purple","blue","orange"),cex=0.65,lty=1,lwd=2)
2b. Visually inspect the infered E.coli sRNA network. Fig 3B was created in Cytoscape. Here we use the igraph package to get a raw draft.
#Load the output files of the Inferelator run for E. coli
load("../Inferelator_output_files/Ecoli_8sRNAs/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/params_and_input.RData")
#Create final network model
source("../Miscellaneous_scripts/create_final_networks.R")[1] 0.3668516#Create table with sRNA-mRNA interactions for all eight sRNAs
sRNAs<-colnames(IN$priors.mat)[140:147]
#Function to remove the loci part of gene IDs used in expression, confidence score and betas matrices
remove.locus.tag<-function(geneNames)
{
output<-sapply(1:length(geneNames),function(x){strsplit(as.character(geneNames)[x],split = "_")[[1]][1]})
output
}
#Function to create the sRNA network - it uses final network model created above
create.network<-function(regulatorsNames,recoveredInteractionsMatrix,novelInteractionsMatrix,betas)
{
output.network<-c()
for(g in regulatorsNames)
{
recovered.targets<-names(which(recoveredInteractionsMatrix[,g]!=0))
novel.targets<-names(which(novelInteractionsMatrix[,g]!=0))
g<-remove.locus.tag(g)
if(length(recovered.targets)>0)
{
output.network<-rbind(output.network,cbind(rep(g,length(recovered.targets)),remove.locus.tag(recovered.targets)
,rep("prior",length(recovered.targets))))
}
if(length(novel.targets)>0)
{
output.network<-rbind(output.network,cbind(rep(g,length(novel.targets)),remove.locus.tag(novel.targets)
,rep("novel",length(novel.targets))))
}
}
colnames(output.network)<-c("Regulator","Target","Interaction")
output.network
}
sRNAs.network<-create.network(sRNAs,recovered.interactions.matrix,novel.interactions.matrix)
sRNA.network.igraph.format<-graph_from_data_frame(sRNAs.network[,c("Regulator","Target")]
,union(sRNAs.network[,"Regulator"],sRNAs.network[,"Target"]), directed = T)
selected.network.layout <- layout_nicely(sRNA.network.igraph.format)
plot(sRNA.network.igraph.format,layout = selected.network.layout, edge.arrow.size =0.2,vertex.label.cex=0.3,vertex.size=9,main="Fig 3B draft")
2c. Compare the regression coefficients (betas) of sRNA-mRNA and TF-gene interactions (Fig 3C)
betas.tfs<-c()
betas.sRNAs<-c()
#Save TFs names
TFs<-setdiff(colnames(normalized.confidence.matrix),sRNAs)
#Combined recovered and novel interactions in a single matrix
all.predictions.matrix<-recovered.interactions.matrix + novel.interactions.matrix
betas.tfs<-abs(combined.betas[,TFs][which(all.predictions.matrix[,TFs]!=0)])
betas.sRNAs<-abs(combined.betas[,sRNAs][which(all.predictions.matrix[,sRNAs]!=0)])
temporal.matrix<-rbind(cbind(betas.tfs,rep("TFs",length(betas.tfs))),cbind(betas.sRNAs,rep("sRNAs",length(betas.sRNAs))))
colnames(temporal.matrix)<-c("Betas","Type")
temporal.matrix<-as.data.frame(temporal.matrix)
temporal.matrix$Betas<-as.numeric(as.vector(temporal.matrix$Betas))
p <- ggplot(temporal.matrix, aes(x=Type, y=Betas,fill=Type)) + geom_violin(show.legend = F)
p + stat_summary(fun.y=median, geom="point", size=2,show.legend = F) + coord_flip()
2d. Plot experimental support of the inferred E. coli sRNA regulons (Fig 3D)
#This figure is generated using data in Supplementary Dataset 1
accuracy.sRNA.regulons<-c()
accuracy.CyaR.regulon<-cbind(c(6,1,7,0),c(0,4,4,0))
accuracy.sRNA.regulons<-rbind(accuracy.sRNA.regulons,accuracy.CyaR.regulon)
accuracy.FnrS.regulon<-cbind(c(7,1,8,0),c(0,6,6,0))
accuracy.sRNA.regulons<-rbind(accuracy.sRNA.regulons,accuracy.FnrS.regulon)
accuracy.GcvB.regulon<-cbind(c(6,16,22,0),c(0,11,11,0))
accuracy.sRNA.regulons<-rbind(accuracy.sRNA.regulons,accuracy.GcvB.regulon)
accuracy.OmrA.regulon<-cbind(c(4,1,5,0),c(0,0,0,0))
accuracy.sRNA.regulons<-rbind(accuracy.sRNA.regulons,accuracy.OmrA.regulon)
accuracy.RybB.regulon<-cbind(c(5,0,5,0),c(0,3,3,0))
accuracy.sRNA.regulons<-rbind(accuracy.sRNA.regulons,accuracy.RybB.regulon)
accuracy.RyhB.regulon<-cbind(c(6,5,11,0),c(0,8,8,0))
accuracy.sRNA.regulons<-rbind(accuracy.sRNA.regulons,accuracy.RyhB.regulon)
accuracy.Spot42.regulon<-cbind(c(4,5,9,0),c(0,0,0,0))
accuracy.sRNA.regulons<-rbind(accuracy.sRNA.regulons,accuracy.Spot42.regulon)
#Create barplot
barplot(t(accuracy.sRNA.regulons),col=rep(c(rgb(90,180,172,maxColorValue = 255),rgb(216,179,101,maxColorValue = 255)),28)
,names=rep(c("Known","Novel","Full",""),7),las=2,cex.axis = 1.25,ylab="# Targets")
legend("topleft",
legend = c("Supported", "Not Supported"),
fill = c(rgb(90,180,172,maxColorValue = 255),rgb(216,179,101,maxColorValue = 255)),bty="n")
2e. Plot performance of the Inferelator with noisy sRNA priors (Fig 3E & Fig S2)
#Load Inferelator run without noisy sRNA priors
load("../Inferelator_output_files/Ecoli_8sRNAs/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/params_and_input.RData")
#Create final model
source("../Miscellaneous_scripts/create_final_networks.R")[1] 0.3668516#Save manually selected sRNA priors used as input for the Inferelator
original.sRNAs.gs<-IN$priors.mat[,sRNAs]
#Recovered sRNA priors in the Inferelator run without noisy priors
recovered.sRNA.priors.no.noise<-recovered.interactions.matrix[,sRNAs]
#The ratio of true:false ratio in the noisy sRNA priors (1:1,1:2,1:5)
noise.level<-c(1,2,5)
#Create matrix to store average experimental support rate of recovered sRNA priors
mean.support.rate.recovered.priors.matrix<-matrix(nrow=3,ncol=length(sRNAs)+1,0,dimnames = list(paste("n",noise.level,sep=""),c(sRNAs,"Random")))
#The expected average ratio from random selections
mean.support.rate.recovered.priors.matrix[,9]<-c(1/2,1/3,1/6)
#Create matrix to store average number of recovered sRNA priors
mean.number.recovered.priors<-matrix(nrow=3,ncol=length(sRNAs),0,dimnames = list(paste("n",noise.level,sep=""),sRNAs))
#Create matrix to store average number of experimentally supported recovered sRNA priors
mean.number.true.recovered.priors<-matrix(nrow=3,ncol=length(sRNAs),0,dimnames = list(paste("n",noise.level,sep=""),sRNAs))
#This loop computes the number of recovered priors and the exp. support rate of the recovered priors for the Inferelator runs that used noisy sRNA priors (ten runs for each noise level)
for(k in noise.level)
{
#Create matrices to store the information of the ten Inferelator runs
temporal.recovered.sRNA.priors.matrix<-matrix(nrow=10,ncol=length(sRNAs),0,dimnames = list(paste("r",1:10,sep=""),sRNAs))
temporal.recovered.true.sRNA.priors.matrix<-matrix(nrow=10,ncol=length(sRNAs),0,dimnames = list(paste("r",1:10,sep=""),sRNAs))
temporal.suppport.rate.recovered.sRNA.priors.matrix<-matrix(nrow=10,ncol=length(sRNAs),0,dimnames = list(paste("r",1:10,sep=""),sRNAs))
for(r in 1:10)
{
path.to.noisy.inferelator.output<-"../Inferelator_output_files/Ecoli_noisy_sRNA_priors/eco_sRNA_strong_m3F_unaveraged_noisyPriors_n"
load(paste(path.to.noisy.inferelator.output,k,"_r",r,"/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData",sep=""))
load(paste(path.to.noisy.inferelator.output,k,"_r",r,"/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData",sep=""))
load(paste(path.to.noisy.inferelator.output,k,"_r",r,"/params_and_input.RData",sep=""))
source("../Miscellaneous_scripts/create_final_networks.R")
for(s in sRNAs)
{
true.sRNA.priors<-names(which(original.sRNAs.gs[,s]!=0))
#The curated.gs.matrix object was created by the create_final_networks.R script used above
noisy.sRNA.priors<-names(which(curated.gs.matrix[,s]!=0))
#The recovered.sRNA.priors object was created by the create_final_networks.R script used above
recovered.sRNA.priors<-names(which(recovered.interactions.matrix[,s]!=0))
temporal.recovered.sRNA.priors.matrix[paste("r",r,sep=""),s]<-length(recovered.sRNA.priors)
temporal.recovered.true.sRNA.priors.matrix[paste("r",r,sep=""),s]<-length(intersect(recovered.sRNA.priors,true.sRNA.priors))
temporal.suppport.rate.recovered.sRNA.priors.matrix[paste("r",r,sep=""),s]<-temporal.recovered.true.sRNA.priors.matrix[paste("r",r,sep=""),s]/length(recovered.sRNA.priors)
}
}
#Compute average acrros the ten runs
mean.number.recovered.priors[paste("n",k,sep=""),1:8]<-colMeans(temporal.recovered.sRNA.priors.matrix,na.rm = T)
mean.number.true.recovered.priors[paste("n",k,sep=""),1:8]<-colMeans(temporal.recovered.true.sRNA.priors.matrix,na.rm=T)
mean.support.rate.recovered.priors.matrix[paste("n",k,sep=""),1:8]<-colMeans(temporal.suppport.rate.recovered.sRNA.priors.matrix,na.rm=T)
}[1] 0.3483171
[1] 0.3581108
[1] 0.3528762
[1] 0.3535159
[1] 0.3515724
[1] 0.3514696
[1] 0.3531012
[1] 0.3540409
[1] 0.3500886
[1] 0.3556734
[1] 0.3395171
[1] 0.3387281
[1] 0.3421028
[1] 0.3356091
[1] 0.3393354
[1] 0.3403037
[1] 0.3419162
[1] 0.3375457
[1] 0.3405978
[1] 0.3380823
[1] 0.3158115
[1] 0.3157688
[1] 0.3167973
[1] 0.3146657
[1] 0.3164843
[1] 0.3163847
[1] 0.3151067
[1] 0.3167479
[1] 0.3141667
[1] 0.3186626par(mfrow=c(1,1))
boxplot(ylim=c(0,1),t(mean.support.rate.recovered.priors.matrix),col="white",outline=F,boxlty=0,whisklty = 0, staplelty = 0, names=c("1:1","1:2","1:5"),frame=F,
xlab="Supported : Unsupported priors",ylab="Proportion of recovered priors w/ support",cex.lab=1.1,main="Fig 3E")
dots.colors<-c("blue","red","dark green","deeppink","orange","cyan","purple","brown","gray")
dots.shapes<-c(2,1,3:6,0,7,8)
for(r in 1:(length(sRNAs)+1))
{
points(y=mean.support.rate.recovered.priors.matrix[,r],x=c(1,2,3),pch=dots.shapes[r],col=dots.colors[r])
}legend("bottomleft",inset=0.03,box.lty=0.1,box.lwd=0.2,
c("RyhB(12)","Spot42(12)","GcvB(9)","MicA(10)","OmrA(6)","CyaR(6)","RybB(10)","FnrS(10)","Random"),col=dots.colors,cex=0.65,pch=dots.shapes)
#Create Fig S2 (because MicA targets were not predicted in the original run- we remove MicA from the following analysis)
sRNAs.figS2<-sRNAs[-4]
par(mfrow=c(1,2))
#Panel A
boxplot(ylim=c(0,1.2),t(mean.number.recovered.priors[,sRNAs.figS2])/colSums(abs(recovered.sRNA.priors.no.noise[,sRNAs.figS2])),col="white",outline=F,boxlty=0,whisklty = 0, staplelty = 0, names=c("1:1","1:2","1:5"),frame=F,xlab="Supported : Unsupported priors",ylab="Total recovered priors (compared to run w/out false priors)",cex.lab=1.1,main="Fig S2A")
for(x in 1:(length(sRNAs.figS2)))
{
points(y=mean.number.recovered.priors[,sRNAs.figS2[x]]/sum(abs(recovered.sRNA.priors.no.noise[,sRNAs.figS2[x]])),x=c(1,2,3),pch=dots.shapes[-4][x],col=dots.colors[-4][x])
}#Panel B
boxplot(ylim=c(0,1.2),t(mean.number.true.recovered.priors[,sRNAs.figS2])/colSums(abs(recovered.sRNA.priors.no.noise[,sRNAs.figS2])),col="white",outline=F,boxlty=0,whisklty = 0, staplelty = 0, names=c("1:1","1:2","1:5"),frame=F,xlab="Supported : Unsupported priors",ylab="True recovered priors (compared to no false priors run)",cex.lab=1.1,main="Fig S2B")
for(x in 1:(length(sRNAs.figS2)))
{
points(y=mean.number.true.recovered.priors[,sRNAs.figS2[x]]/sum(abs(recovered.sRNA.priors.no.noise[,sRNAs.figS2[x]])),x=c(1,2,3),pch=dots.shapes[-4][x],col=dots.colors[-4][x])
}legend("topright",inset=0.03,box.lty=0.1,box.lwd=0.2,
c("RyhB(12)","Spot42(12)","GcvB(9)","OmrA(6)","CyaR(6)","RybB(10)","FnrS(10)"),cex=0.65,pch=dots.shapes[1:8][-4],col=dots.colors[1:8][-4])
- Inspect sRNA regulons inferred using CopraRNA (Wrigth et al. 2013) sRNA priors (Fig 4)
#Create Fig 4B using Table S2
copraRNA.100.support.rate<-c(.29,.3,.17)
copraRNA.100.recovered.priors.support.rate<-c(1,.67,.33)
copraRNA.pval.support.rate<-c(.35,.46,.22)
copraRNA.pval.recovered.priors.support.rate<-c(.4,.82,.25)
copraRNA.enriched.support.rate<-c(.5,.56,.26)
copraRNA.enriched.recovered.priors.support.rate<-c(1,.82,.4)
copraRNA.top15.support.rate<-c(.48,.64,.31)
copraRNA.top15.recovered.priors.support.rate<-c(1,.8,.5)
copraRNA.AND.support.rate<-c(.55,.73,.35)
copraRNA.AND.recovered.priors.support.rate<-c(1,.77,.67)
copraRNA.OR.support.rate<-c(.37,.41,.2)
copraRNA.OR.recovered.priors.support.rate<-c(1,.8,.29)
copraRNA.support.rate<-c(copraRNA.100.support.rate,copraRNA.pval.support.rate,copraRNA.enriched.support.rate,
copraRNA.top15.support.rate,copraRNA.AND.support.rate,copraRNA.OR.support.rate)
copraRNA.recovered.priors.support.rate<-c(copraRNA.100.recovered.priors.support.rate,copraRNA.pval.recovered.priors.support.rate,
copraRNA.enriched.recovered.priors.support.rate,copraRNA.top15.recovered.priors.support.rate,
copraRNA.AND.recovered.priors.support.rate,copraRNA.OR.recovered.priors.support.rate)
plot(x=copraRNA.support.rate,y=copraRNA.recovered.priors.support.rate,col=rep(c("purple","green","orange"),6),xlab="Experimental support rate - CopraRNA priors",ylab="Experimental support rate - Recovered priors",xlim=c(0,1),ylim=c(0,1),
pch=rep(c(15,16,17,18,19,4),each=3))
lines(x=c(0,1),y=c(0,1),col="red",lty=2)legend('bottomright', c("RyhB","GcvB","Spot 42"), col=c("purple","green","orange"),pch=16,bty="n")
#Plot the inferred RyhB regulon when CopraRNA predictions associated with enriched functional terms were used as priors (Fig 4C)
#Here we use the igraph package to get raw drafts
#Load the output files of the corresponding Inferelator run
load("../Inferelator_output_files/Ecoli_CopraRNA_sRNA_priors/ryhB_silico_2_BBSR_1.1_100_0/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_CopraRNA_sRNA_priors/ryhB_silico_2_BBSR_1.1_100_0/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_CopraRNA_sRNA_priors/ryhB_silico_2_BBSR_1.1_100_0/params_and_input.RData")
#Create final network model
source("../Miscellaneous_scripts/create_final_networks.R")[1] 0.3583497#Save the inferred RyhB regulon
RyhB.regulon<-create.network("ryhB_b4451_4",recovered.interactions.matrix,novel.interactions.matrix)
RyhB.regulon.igraph.format<-graph_from_data_frame(RyhB.regulon[,1:2], union(RyhB.regulon[,"Regulator"],RyhB.regulon[,"Target"]), directed = T)
plot(RyhB.regulon.igraph.format,layout = layout_nicely(RyhB.regulon.igraph.format), edge.arrow.size =0.4,vertex.label.cex=1,vertex.size=30,main="Fig 4C",vertex.shape=c("square",rep("circle",nrow(RyhB.regulon))),vertex.label.color=c("blue",rep("black",length(grep("prior",RyhB.regulon[,3]))),rep("white",length(grep("novel",RyhB.regulon[,3])))))
#Plot the inferred GcvB regulon when CopraRNA predictions with p-value <= 0.01 were used as priors (Fig 4D)
#Load the output files of the corresponding Inferelator run
load("../Inferelator_output_files/Ecoli_CopraRNA_sRNA_priors/gcvB_silico_1_BBSR_1.1_100_0/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_CopraRNA_sRNA_priors/gcvB_silico_1_BBSR_1.1_100_0/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_CopraRNA_sRNA_priors/gcvB_silico_1_BBSR_1.1_100_0/params_and_input.RData")
#Create final network model
source("../Miscellaneous_scripts/create_final_networks.R")[1] 0.3591327#Save the inferred GcvB regulon
GcvB.regulon<-create.network("gcvB_b4443_12",recovered.interactions.matrix,novel.interactions.matrix)
GcvB.regulon.igraph.format<-graph_from_data_frame(GcvB.regulon[,1:2], union(GcvB.regulon[,"Regulator"],GcvB.regulon[,"Target"]), directed = T)
plot(GcvB.regulon.igraph.format,layout = layout_nicely(GcvB.regulon.igraph.format), edge.arrow.size =0.4,vertex.label.cex=0.8,vertex.size=21,main="Fig 4D",vertex.shape=c("square",rep("circle",nrow(GcvB.regulon))),vertex.label.color=c("blue",rep("black",length(grep("prior",GcvB.regulon[,3]))),rep("white",length(grep("novel",GcvB.regulon[,3])))))
#Plot the inferred Spot 42 regulon when CopraRNA predictions with p-value <= 0.01 & associated with enriched functional terms were used as priors (Fig 4E)
#Load the output files of the corresponding Inferelator run
load("../Inferelator_output_files/Ecoli_CopraRNA_sRNA_priors/spf_silico_3_BBSR_1.1_100_0/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_CopraRNA_sRNA_priors/spf_silico_3_BBSR_1.1_100_0/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_CopraRNA_sRNA_priors/spf_silico_3_BBSR_1.1_100_0/params_and_input.RData")
#Create final network model
source("../Miscellaneous_scripts/create_final_networks.R")[1] 0.3607158#Save the inferred Spot 42 regulon
Spot42.regulon<-create.network("spf_b3864_15",recovered.interactions.matrix,novel.interactions.matrix)
Spot42.regulon.igraph.format<-graph_from_data_frame(Spot42.regulon[,1:2], union(Spot42.regulon[,"Regulator"],Spot42.regulon[,"Target"]), directed = T)
plot(Spot42.regulon.igraph.format,layout = layout_nicely(Spot42.regulon.igraph.format), edge.arrow.size =0.4,vertex.label.cex=0.8,vertex.size=30,main="Fig 4E",vertex.shape=c("square",rep("circle",nrow(Spot42.regulon))),vertex.label.color=c("blue",rep("black",length(grep("prior",Spot42.regulon[,3]))),rep("white",length(grep("novel",Spot42.regulon[,3])))))
- Create sRNA regulons shown in Fig 5. This figure was created in Cytoscape. Here we use the igraph package to get raw drafts.
#Load the output files of the Inferelator run for E. coli
load("../Inferelator_output_files/Ecoli_8sRNAs/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/params_and_input.RData")
#Create final network model
source("../Miscellaneous_scripts/create_final_networks.R")[1] 0.3668516#Create table with sRNA-mRNA interactions for all eight sRNAs
sRNAs<-colnames(IN$priors.mat)[140:147]
sRNAs.network<-create.network(sRNAs,recovered.interactions.matrix,novel.interactions.matrix)
#Plot the inferred Spot 42 regulon
Spot42.regulon<-sRNAs.network[grep("spf",sRNAs.network[,1]),]
Spot42.regulon.igraph.format<-graph_from_data_frame(Spot42.regulon[,1:2], union(Spot42.regulon[,"Regulator"],Spot42.regulon[,"Target"]), directed = T)
plot(Spot42.regulon.igraph.format,layout = layout_nicely(Spot42.regulon.igraph.format), edge.arrow.size =0.4,vertex.label.cex=1,vertex.size=30,main="Fig 5A",vertex.shape=c("square",rep("circle",nrow(Spot42.regulon))),vertex.label.color=c("blue",rep("black",length(grep("prior",Spot42.regulon[,3]))),rep("white",length(grep("novel",Spot42.regulon[,3])))))
#Plot the inferred GcvB regulon
GcvB.regulon<-sRNAs.network[grep("gcvB",sRNAs.network[,1]),]
GcvB.regulon.igraph.format<-graph_from_data_frame(GcvB.regulon[,1:2], union(GcvB.regulon[,"Regulator"],GcvB.regulon[,"Target"]), directed = T)
plot(GcvB.regulon.igraph.format,layout = layout_nicely(GcvB.regulon.igraph.format), edge.arrow.size =0.4,vertex.label.cex=1,vertex.size=30,main="Fig 5B",vertex.shape=c("square",rep("circle",nrow(GcvB.regulon))),vertex.label.color=c("blue",rep("black",length(grep("prior",GcvB.regulon[,3]))),rep("white",length(grep("novel",GcvB.regulon[,3])))))
#Plot the inferred PrrF regulon in P. aeruginosa
#Load the output files of the Inferelator run for P. aeruginosa
load("../Inferelator_output_files/PrrF_Paeruginosa/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/PrrF_Paeruginosa/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/PrrF_Paeruginosa/params_and_input.RData")
#Create final network model
source("../Miscellaneous_scripts/create_final_networks.R")[1] 0.265658#Create table with PrrF-mRNA interactions
PrrF.regulon<-create.network("PrrF",recovered.interactions.matrix,novel.interactions.matrix)
PrrF.regulon.igraph.format<-graph_from_data_frame(PrrF.regulon[,1:2], union(PrrF.regulon[,"Regulator"],PrrF.regulon[,"Target"]), directed = T)
plot(PrrF.regulon.igraph.format,layout = layout_nicely(PrrF.regulon.igraph.format), edge.arrow.size =0.4,vertex.label.cex=0.65,vertex.size=30,main="Fig 5C",vertex.shape=c("square",rep("circle",nrow(PrrF.regulon))),vertex.label.color=c("blue",rep("black",length(grep("prior",PrrF.regulon[,3]))),rep("white",length(grep("novel",PrrF.regulon[,3])))))
#Plot the inferred FsrA regulon in B. subtilis
#Load the output files of the Inferelator run for B. subtilis
load("../Inferelator_output_files/FsrA_Bsubtilis/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/FsrA_Bsubtilis/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/FsrA_Bsubtilis/params_and_input.RData")
#Create final network model
source("../Miscellaneous_scripts/create_final_networks.R")[1] 0.7639555#Create table with PrrF-mRNA interactions
FsrA.regulon<-create.network(FsrA,recovered.interactions.matrix,novel.interactions.matrix)
#Replace locus ID with common sRNA name
FsrA.regulon[,1]<-"FsrA"
FsrA.regulon.igraph.format<-graph_from_data_frame(FsrA.regulon[,1:2], union(FsrA.regulon[,"Regulator"],FsrA.regulon[,"Target"]), directed = T)
plot(FsrA.regulon.igraph.format,layout = layout_nicely(FsrA.regulon.igraph.format), edge.arrow.size =0.4,vertex.label.cex=0.55,vertex.size=32,main="Fig 5D",vertex.shape=c("square",rep("circle",nrow(FsrA.regulon))),vertex.label.color=c("blue",rep("black",length(grep("prior",FsrA.regulon[,3]))),rep("white",length(grep("novel",FsrA.regulon[,3])))))
#Plot the inferred RsaE regulon in S. aureus
#Load the output files of the Inferelator run for S. aures
load("../Inferelator_output_files/RsaE_Saureus/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/RsaE_Saureus/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/RsaE_Saureus/params_and_input.RData")
#Create final network model
source("../Miscellaneous_scripts/create_final_networks.R")[1] 0.5834941#Create table with RsaE-mRNA interactions
RsaE.recovered.targets<-names(which(recovered.interactions.matrix[,RsaE]!=0))
RsaE.recovered.targets<-sapply(1:length(RsaE.recovered.targets), function(x){gsub("SAOUHSC","SA",RsaE.recovered.targets[x])})
RsaE.novel.targets<-names(which(novel.interactions.matrix[,RsaE]!=0))
RsaE.novel.targets<-sapply(1:length(RsaE.novel.targets), function(x){gsub("SAOUHSC","SA",RsaE.novel.targets[x])})
RsaE.regulon<-rbind(cbind(rep("RsaE",length(RsaE.recovered.targets)),RsaE.recovered.targets,rep("priors",length(RsaE.recovered.targets))),cbind(rep("RsaE",length(RsaE.novel.targets)),RsaE.novel.targets,rep("novel",length(RsaE.novel.targets))))
colnames(RsaE.regulon)<-c("Regulator","Target","Interaction")
RsaE.regulon.igraph.format<-graph_from_data_frame(RsaE.regulon[,1:2], union(RsaE.regulon[,"Regulator"],RsaE.regulon[,"Target"]), directed = T)
plot(RsaE.regulon.igraph.format,layout = layout_nicely(RsaE.regulon.igraph.format), edge.arrow.size =0.4,vertex.label.cex=0.55,vertex.size=32,main="Fig 5E",vertex.shape=c("square",rep("circle",nrow(RsaE.regulon))),vertex.label.color=c("blue",rep("black",length(grep("prior",RsaE.regulon[,3]))),rep("white",length(grep("novel",RsaE.regulon[,3])))))
---
title: "Computational analyses for *Inference of bacterial small RNA regulatory networks and integration with transcription factor driven regulatory networks*"
output: html_notebook
author: Mario Arrieta-Ortiz
date: November 24, 2019
---
0. Load required libraries
```{r message=FALSE}
library(ggplot2)
library(Hmisc)
library(multtest)
library(network)
library(igraph)
```
1a. Examine the relation between the transcription of bacterial sRNAs and their regulatory activities - Part I (Fig 1)
```{r}
#Name of sRNAs shown in Fig 1 A-B
fig1.sRNAs<-c("spf","ryhB")
#Load the output of the Inferelator run
load("../Inferelator_output_files/Ecoli_8sRNAs/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/params_and_input.RData")
#Extract expression response and design matrices
expression.response.matrix<-IN$final_response_matrix
expression.design.matrix<-IN$final_design_matrix
#This is the gold standard (prior) network
gs.matrix<-IN$gs.mat
#Matrix with regulators (TFs & sRNAs) activities
regulators.activities.matrix<-IN$tf.activities[[1]]
par(mfrow=c(1,2))
for(i in fig1.sRNAs)
{
  sRNA.activities<-regulators.activities.matrix[grep(i,rownames(regulators.activities.matrix)),]
  sRNA.transcriptional.profile<-expression.design.matrix[grep(i,rownames(expression.design.matrix)),]
  sRNA.priors<-rownames(gs.matrix)[which(gs.matrix[,grep(i,colnames(gs.matrix))]!=0)]
  sRNA.regulon.expression<-expression.response.matrix[sRNA.priors,]
  mean.expression.sRNA.regulon<-colMeans(sRNA.regulon.expression)
  plot(x=sRNA.transcriptional.profile,y=mean.expression.sRNA.regulon,col=rgb(1,0,0,1),lwd=1,xlab=paste(i,"transcription",sep=" "),ylab="Mean transcription of targets",cex=1.5, cex.lab=1.5,cex.axis=1.5)
  #Perform linear regression
  lr.sRNA.exp.regulon<-lm(mean.expression.sRNA.regulon ~ as.numeric(sRNA.transcriptional.profile))
  abline(lr.sRNA.exp.regulon,col="darkred",lwd=3,lty=2)
  plot(x=sRNA.activities,y=mean.expression.sRNA.regulon,col=rgb(1,0,0,1),lwd=1,xlab=paste(capitalize(i),"activity",sep=" "),ylab="",cex=1.5, cex.lab=1.5,cex.axis=1.5)
  #Perform linear regression
  lr.sRNA.activity.regulon<-lm(mean.expression.sRNA.regulon ~ as.numeric(sRNA.activities))
  abline(lr.sRNA.activity.regulon,col="darkred",lwd=3,lty=2)
}
#Create similar figure for FnrS activity (its transcriptional profile is not present in the expression matrix) 
  sRNA.activities<-regulators.activities.matrix[grep("FnrS",rownames(regulators.activities.matrix)),]
  sRNA.priors<-rownames(gs.matrix)[which(gs.matrix[,grep("FnrS",colnames(gs.matrix))]!=0)]
  sRNA.regulon.expression<-expression.response.matrix[sRNA.priors,]
  mean.expression.sRNA.regulon<-colMeans(sRNA.regulon.expression)
  plot(x=sRNA.activities,y=mean.expression.sRNA.regulon,col=rgb(1,0,0,1),lwd=1,xlab="FnrS activity",ylab="Mean transcription of targets",cex=1.5, cex.lab=1.5,cex.axis=1.5, main="Fig 1D")
  #Perform linear regression
  lr.sRNA.activity.regulon<-lm(mean.expression.sRNA.regulon ~ as.numeric(sRNA.activities))
  abline(lr.sRNA.activity.regulon,col="darkred",lwd=3,lty=2)
  #Create Fig 1C
  #Define correlation between sRNAs and their priors
  sRNAs<-colnames(IN$priors.mat)[140:147]
  correlation.sRNA.activities.targets.expression<-c()
  correlation.sRNA.expression.targets.expression<-c()
  for(t in sRNAs[-8])
  {
    sRNA.activities<-regulators.activities.matrix[t,]
    sRNA.expression<-expression.design.matrix[t,]
    sRNA.priors<-names(which(gs.matrix[,t]!=0))
    for(g in sRNA.priors)
    {
    correlation.sRNA.activities.targets.expression<-c(correlation.sRNA.activities.targets.expression,
                                                      cor(sRNA.activities,expression.response.matrix[g,]))
    correlation.sRNA.expression.targets.expression<-c(correlation.sRNA.expression.targets.expression,
                                                      cor(sRNA.expression,expression.response.matrix[g,]))  
    }
  }
temporal.matrix<-rbind(cbind(correlation.sRNA.expression.targets.expression,rep("Expression",length(correlation.sRNA.expression.targets.expression))),cbind(correlation.sRNA.activities.targets.expression,rep("Activity",length(correlation.sRNA.activities.targets.expression))))
temporal.matrix<-as.data.frame(temporal.matrix)
colnames(temporal.matrix)<-c("PearsonCorrelation","Type")
temporal.matrix$PearsonCorrelation<-as.numeric(as.vector(temporal.matrix$PearsonCorrelation))
p <- ggplot(temporal.matrix, aes(x=Type, y=PearsonCorrelation,fill=Type)) + geom_violin()
p + stat_summary(fun.y=median, geom="point", size=2, color="purple") + ggtitle("Fig 1C")
```

1b. Examine the relation between the transcription of bacterial sRNAs and their regulatory activities - Part II (Fig S1)
```{r}
#Create Fig S1A-E
other.ecoli.sRNAs<-c("cyaR","micA","gcvB","omrA","rybB")
#Load the output of the Inferelator
load("../Inferelator_output_files/Ecoli_8sRNAs/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/params_and_input.RData")
#Extract expression response and design matrices
expression.response.matrix<-IN$final_response_matrix
expression.design.matrix<-IN$final_design_matrix
gs.matrix<-IN$gs.mat
regulators.activities.matrix<-IN$tf.activities[[1]]
par(mfrow=c(1,2))
for(i in other.ecoli.sRNAs)
{
  sRNA.activities<-regulators.activities.matrix[grep(i,rownames(regulators.activities.matrix)),]
  sRNA.transcriptional.profile<-expression.design.matrix[grep(i,rownames(expression.design.matrix)),]
  sRNA.priors<-rownames(gs.matrix)[which(gs.matrix[,grep(i,colnames(gs.matrix))]!=0)]
  sRNA.regulon.expression<-expression.response.matrix[sRNA.priors,]
  mean.expression.sRNA.regulon<-colMeans(sRNA.regulon.expression)
  plot(x=sRNA.transcriptional.profile,y=mean.expression.sRNA.regulon,col=rgb(1,0,0,1),lwd=1,xlab=paste(i,"transcription",sep=" "),ylab="Mean transcription of targets",cex=1.5, cex.lab=1.5,cex.axis=1.5)
  #Perform linear regression
  lr.sRNA.exp.regulon<-lm(mean.expression.sRNA.regulon ~ as.numeric(sRNA.transcriptional.profile))
  abline(lr.sRNA.exp.regulon,col="darkred",lwd=3,lty=2)
  plot(x=sRNA.activities,y=mean.expression.sRNA.regulon,col=rgb(1,0,0,1),lwd=1,xlab=paste(capitalize(i),"activity",sep=" "),ylab="",cex=1.5, cex.lab=1.5,cex.axis=1.5)
  #Perform linear regression
  lr.sRNA.activity.regulon<-lm(mean.expression.sRNA.regulon ~ as.numeric(sRNA.activities))
  abline(lr.sRNA.activity.regulon,col="darkred",lwd=3,lty=2)
}
#Create Fig S1F
#Load B. subtilis run
load("../Inferelator_output_files/FsrA_Bsubtilis/params_and_input.RData")
load("../Inferelator_output_files/FsrA_Bsubtilis/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/FsrA_Bsubtilis/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
#Extract expression response and design matrices
expression.response.matrix<-IN$final_response_matrix
expression.design.matrix<-IN$final_design_matrix
gs.matrix<-IN$gs.mat
regulators.activities.matrix<-IN$tf.activities[[1]]
FsrA<-"new_1483539_1483640"
sRNA.activities<-regulators.activities.matrix[FsrA,]
sRNA.transcriptional.profile<-expression.design.matrix[FsrA,]
sRNA.priors<-rownames(gs.matrix)[which(gs.matrix[,FsrA]!=0)]
sRNA.regulon.expression<-expression.response.matrix[sRNA.priors,]
mean.expression.sRNA.regulon<-colMeans(sRNA.regulon.expression)
plot(x=sRNA.transcriptional.profile,y=mean.expression.sRNA.regulon,col=rgb(1,0,0,1),lwd=1,xlab="fsrA transcription",ylab="Mean transcription of targets",cex=1.5, cex.lab=1.5,cex.axis=1.5, main="Fig S1F")
#Perform linear regression
lr.sRNA.exp.regulon<-lm(mean.expression.sRNA.regulon ~ as.numeric(sRNA.transcriptional.profile))
abline(lr.sRNA.exp.regulon,col="darkred",lwd=3,lty=2)
plot(x=sRNA.activities,y=mean.expression.sRNA.regulon,col=rgb(1,0,0,1),lwd=1,xlab="FsrA activity",ylab="",cex=1.5, cex.lab=1.5,cex.axis=1.5)
#Perform linear regression
lr.sRNA.activity.regulon<-lm(mean.expression.sRNA.regulon ~ as.numeric(sRNA.activities))
abline(lr.sRNA.activity.regulon,col="darkred",lwd=3,lty=2)
#Create Fig S1F
#Load S. aureus run
load("../Inferelator_output_files/RsaE_Saureus/params_and_input.RData")
load("../Inferelator_output_files/RsaE_Saureus/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/RsaE_Saureus/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
#Extract expression response and design matrices
expression.response.matrix<-IN$final_response_matrix
expression.design.matrix<-IN$final_design_matrix
gs.matrix<-IN$gs.mat
regulators.activities.matrix<-IN$tf.activities[[1]]
RsaE<-"new_911368_911465_1"
sRNA.activities<-regulators.activities.matrix[RsaE,]
sRNA.transcriptional.profile<-expression.design.matrix[RsaE,]
sRNA.priors<-rownames(gs.matrix)[which(gs.matrix[,RsaE]!=0)]
sRNA.regulon.expression<-expression.response.matrix[sRNA.priors,]
mean.expression.sRNA.regulon<-colMeans(sRNA.regulon.expression)
plot(x=sRNA.transcriptional.profile,y=mean.expression.sRNA.regulon,col=rgb(1,0,0,1),lwd=1,xlab="rsaE transcription",ylab="Mean transcription of targets",cex=1.5, cex.lab=1.5,cex.axis=1.5, main="Fig S1G")
#Perform linear regression
lr.sRNA.exp.regulon<-lm(mean.expression.sRNA.regulon ~ as.numeric(sRNA.transcriptional.profile))
abline(lr.sRNA.exp.regulon,col="darkred",lwd=3,lty=2)
plot(x=sRNA.activities,y=mean.expression.sRNA.regulon,col=rgb(1,0,0,1),lwd=1,xlab="RsaE activity",ylab="",cex=1.5, cex.lab=1.5,cex.axis=1.5)
#Perform linear regression
lr.sRNA.activity.regulon<-lm(mean.expression.sRNA.regulon ~ as.numeric(sRNA.activities))
abline(lr.sRNA.activity.regulon,col="darkred",lwd=3,lty=2)
```
2a. Evaluate performance of the Inferelator and CLR with and without sRNA activities (Fig 3A)
```{r}
#Plot performance of the Inferelator with sRNA activities
#Load the output files of the Inferelator run for E. coli
load("../Inferelator_output_files/Ecoli_8sRNAs/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/params_and_input.RData")
#Normalize confidence score matrix
source("../Miscellaneous_scripts/create_final_networks.R")
#Save the names of sRNAs to be considered in this analysis (FnrS is not included)
sRNAs<-colnames(IN$priors.mat)[140:146]
#Create matrix with normalized confidence score for selected sRNAs
normalized.confidence.matrix.sRNAs<-normalized.confidence.matrix[,sRNAs]
#Convert sRNA priors to zeroes in the normalized confidence score matrix
normalized.confidence.matrix.sRNAs[which(IN$priors.mat[,sRNAs]!=0)]<-0
#Run script to read all candidate sRNA targets supported by experimental data and genomic location (operons)
source("../Miscellaneous_scripts/sRNA_candidate_targets_compilation.R")
#Create vector to save number of supported priors at different ranking positions per sRNA 
performance.inferelator.sRNA.activity<-c()
for(y in 1:100)
{
total.supported.predictions<-0
for(s in colnames(normalized.confidence.matrix.sRNAs))
{
  candidate.targets<-unique(expanded.sRNA.gs[which(expanded.sRNA.gs[,1]== s),2])
  top.targets<-rownames(normalized.confidence.matrix.sRNAs)[order(normalized.confidence.matrix.sRNAs[,s],decreasing=T)[1:y]]
  #Keep only the loci tags
  top.targets<-translate.names(top.targets)
  total.supported.predictions <- total.supported.predictions + length(intersect(candidate.targets,top.targets))
}
performance.inferelator.sRNA.activity<-c(performance.inferelator.sRNA.activity,total.supported.predictions)
}
plot(x=1:100, y=performance.inferelator.sRNA.activity,ylim=c(0,72),xlab="Targets per sRNA",ylab="Supported predictions",col="dark green",type="lines",lwd=2,cex=1.5,cex.lab=1.5,cex.axis=1.5)
#Plot performance of CLR without sRNA activities (sRNA expression instead) using script in the folder of the Inferelator code 
source("../Inferelator_2015.08.05/R_scripts/mi_and_clr.R")
#Remove FnrS from the set of potential regulators
regulators.names<-IN$tf.names[-147]
X<-IN$final_design_matrix[regulators.names,]
#This is the same IN$final_response_matrix because we did not use a metadata file (for time series) 
Y<-IN$final_response_matrix_halftau 
#Calculate mutual information (MI) as done in the Inferelator
Ms <- mi(t(Y), t(X), nbins=PARS$mi.bins, cpu.n=PARS$cores)  
diag(Ms) <- 0
Ms_bg <- mi(t(X), t(X), nbins=PARS$mi.bins, cpu.n=PARS$cores)
diag(Ms_bg) <- 0
#Calculate CLR
clr.matrix = mixedCLR(Ms_bg,Ms)
dimnames(clr.matrix) <- list(rownames(Y), rownames(X))
#Names of sRNA to be considered in this analysis (FnrS is not included)
sRNAs<-colnames(IN$priors.mat)[140:146]
#Create matrix with normalized CLR scores for selected sRNAs
clr.matrix.sRNAs<-clr.matrix[,sRNAs]
performance.clr.sRNA.exp<-c()
for(y in 1:100)
{
total.supported.predictions<-0
for(s in colnames(clr.matrix.sRNAs))
{
  candidate.targets<-unique(expanded.sRNA.gs[which(expanded.sRNA.gs[,1]== s),2])
  top.targets<-rownames(clr.matrix.sRNAs)[order(clr.matrix.sRNAs[,s],decreasing=T)[1:y]]
  top.targets<-translate.names(top.targets)
  total.supported.predictions <- total.supported.predictions + length(intersect(candidate.targets,top.targets))
}
performance.clr.sRNA.exp<-c(performance.clr.sRNA.exp,total.supported.predictions)
}
lines(x=1:100, y=performance.clr.sRNA.exp,col="orange",lwd=2)
#Plot performance of CLR with sRNA activities
X<-IN$tf.activities[[1]]
Y<-IN$final_response_matrix_halftau
#Calculate mutual information (MI) as done in the Inferelator
Ms <- mi(t(Y), t(X), nbins=PARS$mi.bins, cpu.n=PARS$cores)  
diag(Ms) <- 0
Ms_bg <- mi(t(X), t(X), nbins=PARS$mi.bins, cpu.n=PARS$cores)
diag(Ms_bg) <- 0
#Calculate CLR
clr.matrix = mixedCLR(Ms_bg,Ms)
dimnames(clr.matrix) <- list(rownames(Y), rownames(X))
#Create matrix with normalized clr scores for selected sRNAs
clr.matrix.sRNAs<-clr.matrix[,sRNAs]
#Convert sRNA priors to zeroes in the CLR matrix
clr.matrix.sRNAs[which(IN$priors.mat[,sRNAs]!=0)]<-0
performance.clr.sRNA.act<-c()
for(y in 1:100)
{
total.supported.predictions<-0
for(s in colnames(clr.matrix.sRNAs))
{
  candidate.targets<-unique(expanded.sRNA.gs[which(expanded.sRNA.gs[,1]== s),2])
  top.targets<-rownames(clr.matrix.sRNAs)[order(clr.matrix.sRNAs[,s],decreasing=T)[1:y]]
  top.targets<-translate.names(top.targets)
  total.supported.predictions <- total.supported.predictions + length(intersect(candidate.targets,top.targets))
}
performance.clr.sRNA.act<-c(performance.clr.sRNA.act,total.supported.predictions)
}
lines(x=1:100, y=performance.clr.sRNA.act,col="blue",lwd=2)
#Plot performance of the Inferelator without sRNA activities (no TF or sRNA priors were specified)
#Load Inferelator output
load("../Inferelator_output_files/Ecoli_without_regulators_priors/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_0.RData")
load("../Inferelator_output_files/Ecoli_without_regulators_priors/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_0.RData")
load("../Inferelator_output_files/Ecoli_without_regulators_priors/params_and_input.RData")
#Create matrix with normalized confidence score for selected sRNAs
normalized.confidence.matrix<-normalization(as.matrix(comb.confs))
normalized.confidence.matrix.sRNAs<-normalized.confidence.matrix[,sRNAs]
performance.inferelator.sRNA.exp<-c()
for(y in 1:100)
{
total.supported.predictions<-0
for(s in colnames(normalized.confidence.matrix.sRNAs))
{
  candidate.targets<-unique(expanded.sRNA.gs[which(expanded.sRNA.gs[,1]== s),2])
  top.targets<-rownames(normalized.confidence.matrix.sRNAs)[order(normalized.confidence.matrix.sRNAs[,s],decreasing=T)[1:y]]
  top.targets<-translate.names(top.targets)
  total.supported.predictions <- total.supported.predictions + length(intersect(candidate.targets,top.targets))
}
performance.inferelator.sRNA.exp<-c(performance.inferelator.sRNA.exp,total.supported.predictions)
}
lines(x=1:100, y=performance.inferelator.sRNA.exp,col="purple",lwd=2)
#Plot performance of the Inferelator with sRNA activities and shuffled sRNA priors
performance.inferelator.shuffled.sRNA.priors<-c()
for(r in 1:10)
{
  shuffled.path<-paste("../Inferelator_output_files/Ecoli_shuffled_sRNA_priors/Ecoli_shuffled_sRNA_priors",r,"/",sep="")
  #Load Inferelator output files
  load(paste(shuffled.path,"combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData",sep=""))
  load(paste(shuffled.path,"params_and_input.RData",sep=""))
  load(paste(shuffled.path,"betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData",sep=""))
  #Create matrix with normalized confidence score for selected sRNAs
  normalized.confidence.matrix.shuffled.priors<-normalization(as.matrix(comb.confs))
  normalized.confidence.matrix.shuffled.priors.sRNAs<-normalized.confidence.matrix.shuffled.priors[,sRNAs]
  #Convert sRNA priors to zeroes in the normalized confidence score matrix
  normalized.confidence.matrix.shuffled.priors.sRNAs[which(IN$priors.mat[,sRNAs]!=0)]<-0
  temporal.performance<-c()
  for(y in 1:100)
  {
  total.supported.predictions<-0
  for(s in colnames(clr.matrix.sRNAs))
  {
  candidate.targets<-unique(expanded.sRNA.gs[which(expanded.sRNA.gs[,1]== s),2])
  top.targets<-rownames(normalized.confidence.matrix.shuffled.priors.sRNAs)[order(normalized.confidence.matrix.shuffled.priors.sRNAs[,s],decreasing=T)[1:y]]
  top.targets<-translate.names(top.targets)
  total.supported.predictions <- total.supported.predictions + length(intersect(candidate.targets,top.targets))
  }
  temporal.performance<-c(temporal.performance,total.supported.predictions)
  }
  performance.inferelator.shuffled.sRNA.priors<-rbind(performance.inferelator.shuffled.sRNA.priors,temporal.performance)
  }
  lines(x=1:100, y=colMeans(performance.inferelator.shuffled.sRNA.priors),col="dark grey",lwd=2)
  legend("topleft",inset=0.03,box.lty=0.1,box.lwd=0.2,c("BBSR.SRA","BBSR.SRA.Shuffled","BBSR","CLR.SRA","CLR"),col=c("dark green","dark grey","purple","blue","orange"),cex=0.65,lty=1,lwd=2)
```
2b. Visually inspect the infered E.coli sRNA network. Fig 3B was created in Cytoscape. Here we use the igraph package to get a raw draft.
```{r}
#Load the output files of the Inferelator run for E. coli
load("../Inferelator_output_files/Ecoli_8sRNAs/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/params_and_input.RData")
#Create final network model
source("../Miscellaneous_scripts/create_final_networks.R")
#Create table with sRNA-mRNA interactions for all eight sRNAs
sRNAs<-colnames(IN$priors.mat)[140:147]
#Function to remove the loci part of gene IDs used in expression, confidence score and betas matrices
remove.locus.tag<-function(geneNames)
{
  output<-sapply(1:length(geneNames),function(x){strsplit(as.character(geneNames)[x],split = "_")[[1]][1]})
  output
}
#Function to create the sRNA network - it uses final network model created above
create.network<-function(regulatorsNames,recoveredInteractionsMatrix,novelInteractionsMatrix,betas)
{
output.network<-c()
for(g in regulatorsNames)
{
  recovered.targets<-names(which(recoveredInteractionsMatrix[,g]!=0))
  novel.targets<-names(which(novelInteractionsMatrix[,g]!=0))
  g<-remove.locus.tag(g)
  if(length(recovered.targets)>0)
  {
  
  output.network<-rbind(output.network,cbind(rep(g,length(recovered.targets)),remove.locus.tag(recovered.targets)
                                         ,rep("prior",length(recovered.targets))))
  }
  if(length(novel.targets)>0)
  {
  output.network<-rbind(output.network,cbind(rep(g,length(novel.targets)),remove.locus.tag(novel.targets)
                                         ,rep("novel",length(novel.targets))))
  }
}
colnames(output.network)<-c("Regulator","Target","Interaction")
output.network
}
sRNAs.network<-create.network(sRNAs,recovered.interactions.matrix,novel.interactions.matrix)
sRNA.network.igraph.format<-graph_from_data_frame(sRNAs.network[,c("Regulator","Target")]
                                                  ,union(sRNAs.network[,"Regulator"],sRNAs.network[,"Target"]), directed = T)
selected.network.layout <- layout_nicely(sRNA.network.igraph.format)
plot(sRNA.network.igraph.format,layout = selected.network.layout, edge.arrow.size =0.2,vertex.label.cex=0.3,vertex.size=9,main="Fig 3B draft")
```
2c. Compare the regression coefficients (betas) of sRNA-mRNA and TF-gene interactions (Fig 3C)
```{r}
betas.tfs<-c()
betas.sRNAs<-c()
#Save TFs names
TFs<-setdiff(colnames(normalized.confidence.matrix),sRNAs)
#Combined recovered and novel interactions in a single matrix
all.predictions.matrix<-recovered.interactions.matrix + novel.interactions.matrix
betas.tfs<-abs(combined.betas[,TFs][which(all.predictions.matrix[,TFs]!=0)])
betas.sRNAs<-abs(combined.betas[,sRNAs][which(all.predictions.matrix[,sRNAs]!=0)])
temporal.matrix<-rbind(cbind(betas.tfs,rep("TFs",length(betas.tfs))),cbind(betas.sRNAs,rep("sRNAs",length(betas.sRNAs))))
colnames(temporal.matrix)<-c("Betas","Type")
temporal.matrix<-as.data.frame(temporal.matrix)
temporal.matrix$Betas<-as.numeric(as.vector(temporal.matrix$Betas))
p <- ggplot(temporal.matrix, aes(x=Type, y=Betas,fill=Type)) + geom_violin(show.legend = F)
p + stat_summary(fun.y=median, geom="point", size=2,show.legend = F) + coord_flip()
```
2d. Plot experimental support of the inferred E. coli sRNA regulons (Fig 3D)
```{r}
#This figure is generated using data in Supplementary Dataset 1
accuracy.sRNA.regulons<-c()
accuracy.CyaR.regulon<-cbind(c(6,1,7,0),c(0,4,4,0)) 
accuracy.sRNA.regulons<-rbind(accuracy.sRNA.regulons,accuracy.CyaR.regulon)
accuracy.FnrS.regulon<-cbind(c(7,1,8,0),c(0,6,6,0))
accuracy.sRNA.regulons<-rbind(accuracy.sRNA.regulons,accuracy.FnrS.regulon)
accuracy.GcvB.regulon<-cbind(c(6,16,22,0),c(0,11,11,0))
accuracy.sRNA.regulons<-rbind(accuracy.sRNA.regulons,accuracy.GcvB.regulon)
accuracy.OmrA.regulon<-cbind(c(4,1,5,0),c(0,0,0,0))
accuracy.sRNA.regulons<-rbind(accuracy.sRNA.regulons,accuracy.OmrA.regulon)
accuracy.RybB.regulon<-cbind(c(5,0,5,0),c(0,3,3,0))
accuracy.sRNA.regulons<-rbind(accuracy.sRNA.regulons,accuracy.RybB.regulon)
accuracy.RyhB.regulon<-cbind(c(6,5,11,0),c(0,8,8,0))
accuracy.sRNA.regulons<-rbind(accuracy.sRNA.regulons,accuracy.RyhB.regulon)
accuracy.Spot42.regulon<-cbind(c(4,5,9,0),c(0,0,0,0))
accuracy.sRNA.regulons<-rbind(accuracy.sRNA.regulons,accuracy.Spot42.regulon)
#Create barplot
barplot(t(accuracy.sRNA.regulons),col=rep(c(rgb(90,180,172,maxColorValue = 255),rgb(216,179,101,maxColorValue = 255)),28)
        ,names=rep(c("Known","Novel","Full",""),7),las=2,cex.axis = 1.25,ylab="# Targets")
legend("topleft", 
       legend = c("Supported", "Not Supported"), 
       fill = c(rgb(90,180,172,maxColorValue = 255),rgb(216,179,101,maxColorValue = 255)),bty="n")
```
2e. Plot performance of the Inferelator with noisy sRNA priors (Fig 3E & Fig S2) 
```{r}
#Load Inferelator run without noisy sRNA priors
load("../Inferelator_output_files/Ecoli_8sRNAs/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/params_and_input.RData")
#Create final model
source("../Miscellaneous_scripts/create_final_networks.R")
#Save manually selected sRNA priors used as input for the Inferelator
original.sRNAs.gs<-IN$priors.mat[,sRNAs]
#Recovered sRNA priors in the Inferelator run without noisy priors
recovered.sRNA.priors.no.noise<-recovered.interactions.matrix[,sRNAs]
#The ratio of true:false ratio in the noisy sRNA priors (1:1,1:2,1:5)
noise.level<-c(1,2,5)
#Create matrix to store average experimental support rate of recovered sRNA priors
mean.support.rate.recovered.priors.matrix<-matrix(nrow=3,ncol=length(sRNAs)+1,0,dimnames = list(paste("n",noise.level,sep=""),c(sRNAs,"Random")))
#The expected average ratio from  random selections
mean.support.rate.recovered.priors.matrix[,9]<-c(1/2,1/3,1/6)
#Create matrix to store average number of recovered sRNA priors
mean.number.recovered.priors<-matrix(nrow=3,ncol=length(sRNAs),0,dimnames = list(paste("n",noise.level,sep=""),sRNAs))
#Create matrix to store average number of experimentally supported recovered sRNA priors
mean.number.true.recovered.priors<-matrix(nrow=3,ncol=length(sRNAs),0,dimnames = list(paste("n",noise.level,sep=""),sRNAs))
#This loop computes the number of recovered priors and the exp. support rate of the recovered priors for the Inferelator runs that used noisy sRNA priors (ten runs for each noise level)  
for(k in noise.level)
{
  #Create matrices to store the information of the ten Inferelator runs 
  temporal.recovered.sRNA.priors.matrix<-matrix(nrow=10,ncol=length(sRNAs),0,dimnames = list(paste("r",1:10,sep=""),sRNAs))
  temporal.recovered.true.sRNA.priors.matrix<-matrix(nrow=10,ncol=length(sRNAs),0,dimnames = list(paste("r",1:10,sep=""),sRNAs))
  temporal.suppport.rate.recovered.sRNA.priors.matrix<-matrix(nrow=10,ncol=length(sRNAs),0,dimnames = list(paste("r",1:10,sep=""),sRNAs))
  
  for(r in 1:10)
  {
  path.to.noisy.inferelator.output<-"../Inferelator_output_files/Ecoli_noisy_sRNA_priors/eco_sRNA_strong_m3F_unaveraged_noisyPriors_n"
  load(paste(path.to.noisy.inferelator.output,k,"_r",r,"/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData",sep=""))
  load(paste(path.to.noisy.inferelator.output,k,"_r",r,"/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData",sep=""))
  load(paste(path.to.noisy.inferelator.output,k,"_r",r,"/params_and_input.RData",sep=""))
  source("../Miscellaneous_scripts/create_final_networks.R")
  for(s in sRNAs)
  {
    true.sRNA.priors<-names(which(original.sRNAs.gs[,s]!=0))
    #The curated.gs.matrix object was created by the create_final_networks.R script used above
    noisy.sRNA.priors<-names(which(curated.gs.matrix[,s]!=0))
    #The recovered.sRNA.priors object was created by the create_final_networks.R script used above
    recovered.sRNA.priors<-names(which(recovered.interactions.matrix[,s]!=0))
    temporal.recovered.sRNA.priors.matrix[paste("r",r,sep=""),s]<-length(recovered.sRNA.priors)
    temporal.recovered.true.sRNA.priors.matrix[paste("r",r,sep=""),s]<-length(intersect(recovered.sRNA.priors,true.sRNA.priors))
    temporal.suppport.rate.recovered.sRNA.priors.matrix[paste("r",r,sep=""),s]<-temporal.recovered.true.sRNA.priors.matrix[paste("r",r,sep=""),s]/length(recovered.sRNA.priors)
  }
  }
  #Compute average acrros the ten runs
  mean.number.recovered.priors[paste("n",k,sep=""),1:8]<-colMeans(temporal.recovered.sRNA.priors.matrix,na.rm = T)
  mean.number.true.recovered.priors[paste("n",k,sep=""),1:8]<-colMeans(temporal.recovered.true.sRNA.priors.matrix,na.rm=T)
  mean.support.rate.recovered.priors.matrix[paste("n",k,sep=""),1:8]<-colMeans(temporal.suppport.rate.recovered.sRNA.priors.matrix,na.rm=T)
}
par(mfrow=c(1,1))
boxplot(ylim=c(0,1),t(mean.support.rate.recovered.priors.matrix),col="white",outline=F,boxlty=0,whisklty = 0, staplelty = 0, names=c("1:1","1:2","1:5"),frame=F,
  xlab="Supported : Unsupported priors",ylab="Proportion of recovered priors w/ support",cex.lab=1.1,main="Fig 3E")
dots.colors<-c("blue","red","dark green","deeppink","orange","cyan","purple","brown","gray")
dots.shapes<-c(2,1,3:6,0,7,8)
for(r in 1:(length(sRNAs)+1))
{
 points(y=mean.support.rate.recovered.priors.matrix[,r],x=c(1,2,3),pch=dots.shapes[r],col=dots.colors[r])
}
legend("bottomleft",inset=0.03,box.lty=0.1,box.lwd=0.2,
  c("RyhB(12)","Spot42(12)","GcvB(9)","MicA(10)","OmrA(6)","CyaR(6)","RybB(10)","FnrS(10)","Random"),col=dots.colors,cex=0.65,pch=dots.shapes)
#Create Fig S2 (because MicA targets were not predicted in the original run- we remove MicA from the following analysis)
sRNAs.figS2<-sRNAs[-4]
par(mfrow=c(1,2))
#Panel A
boxplot(ylim=c(0,1.2),t(mean.number.recovered.priors[,sRNAs.figS2])/colSums(abs(recovered.sRNA.priors.no.noise[,sRNAs.figS2])),col="white",outline=F,boxlty=0,whisklty = 0, staplelty = 0, names=c("1:1","1:2","1:5"),frame=F,xlab="Supported : Unsupported priors",ylab="Total recovered priors (compared to run w/out false priors)",cex.lab=1.1,main="Fig S2A")
for(x in 1:(length(sRNAs.figS2)))
{
 points(y=mean.number.recovered.priors[,sRNAs.figS2[x]]/sum(abs(recovered.sRNA.priors.no.noise[,sRNAs.figS2[x]])),x=c(1,2,3),pch=dots.shapes[-4][x],col=dots.colors[-4][x])
}
#Panel B
boxplot(ylim=c(0,1.2),t(mean.number.true.recovered.priors[,sRNAs.figS2])/colSums(abs(recovered.sRNA.priors.no.noise[,sRNAs.figS2])),col="white",outline=F,boxlty=0,whisklty = 0, staplelty = 0, names=c("1:1","1:2","1:5"),frame=F,xlab="Supported : Unsupported priors",ylab="True recovered priors (compared to no false priors run)",cex.lab=1.1,main="Fig S2B")
for(x in 1:(length(sRNAs.figS2)))
{
 points(y=mean.number.true.recovered.priors[,sRNAs.figS2[x]]/sum(abs(recovered.sRNA.priors.no.noise[,sRNAs.figS2[x]])),x=c(1,2,3),pch=dots.shapes[-4][x],col=dots.colors[-4][x])
}
legend("topright",inset=0.03,box.lty=0.1,box.lwd=0.2,
  c("RyhB(12)","Spot42(12)","GcvB(9)","OmrA(6)","CyaR(6)","RybB(10)","FnrS(10)"),cex=0.65,pch=dots.shapes[1:8][-4],col=dots.colors[1:8][-4])
```
3. Inspect sRNA regulons inferred using CopraRNA (Wrigth et al. 2013) sRNA priors (Fig 4)
```{r}
#Create Fig 4B using Table S2
copraRNA.100.support.rate<-c(.29,.3,.17)
copraRNA.100.recovered.priors.support.rate<-c(1,.67,.33)
copraRNA.pval.support.rate<-c(.35,.46,.22)
copraRNA.pval.recovered.priors.support.rate<-c(.4,.82,.25)
copraRNA.enriched.support.rate<-c(.5,.56,.26)
copraRNA.enriched.recovered.priors.support.rate<-c(1,.82,.4)
copraRNA.top15.support.rate<-c(.48,.64,.31)
copraRNA.top15.recovered.priors.support.rate<-c(1,.8,.5)
copraRNA.AND.support.rate<-c(.55,.73,.35)
copraRNA.AND.recovered.priors.support.rate<-c(1,.77,.67)
copraRNA.OR.support.rate<-c(.37,.41,.2)
copraRNA.OR.recovered.priors.support.rate<-c(1,.8,.29)
copraRNA.support.rate<-c(copraRNA.100.support.rate,copraRNA.pval.support.rate,copraRNA.enriched.support.rate,
                         copraRNA.top15.support.rate,copraRNA.AND.support.rate,copraRNA.OR.support.rate)
copraRNA.recovered.priors.support.rate<-c(copraRNA.100.recovered.priors.support.rate,copraRNA.pval.recovered.priors.support.rate,
                                          copraRNA.enriched.recovered.priors.support.rate,copraRNA.top15.recovered.priors.support.rate,
                                          copraRNA.AND.recovered.priors.support.rate,copraRNA.OR.recovered.priors.support.rate)

plot(x=copraRNA.support.rate,y=copraRNA.recovered.priors.support.rate,col=rep(c("purple","green","orange"),6),xlab="Experimental support rate - CopraRNA priors",ylab="Experimental support rate - Recovered priors",xlim=c(0,1),ylim=c(0,1),
     pch=rep(c(15,16,17,18,19,4),each=3))
lines(x=c(0,1),y=c(0,1),col="red",lty=2)
legend('bottomright', c("RyhB","GcvB","Spot 42"), col=c("purple","green","orange"),pch=16,bty="n")
#Plot the inferred RyhB regulon when CopraRNA predictions associated with enriched functional terms were used as priors (Fig 4C)
#Here we use the igraph package to get  raw drafts
#Load the output files of the corresponding Inferelator run
load("../Inferelator_output_files/Ecoli_CopraRNA_sRNA_priors/ryhB_silico_2_BBSR_1.1_100_0/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_CopraRNA_sRNA_priors/ryhB_silico_2_BBSR_1.1_100_0/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_CopraRNA_sRNA_priors/ryhB_silico_2_BBSR_1.1_100_0/params_and_input.RData")
#Create final network model
source("../Miscellaneous_scripts/create_final_networks.R")
#Save the inferred RyhB regulon
RyhB.regulon<-create.network("ryhB_b4451_4",recovered.interactions.matrix,novel.interactions.matrix)
RyhB.regulon.igraph.format<-graph_from_data_frame(RyhB.regulon[,1:2], union(RyhB.regulon[,"Regulator"],RyhB.regulon[,"Target"]), directed = T)
plot(RyhB.regulon.igraph.format,layout = layout_nicely(RyhB.regulon.igraph.format), edge.arrow.size =0.4,vertex.label.cex=1,vertex.size=30,main="Fig 4C",vertex.shape=c("square",rep("circle",nrow(RyhB.regulon))),vertex.label.color=c("blue",rep("black",length(grep("prior",RyhB.regulon[,3]))),rep("white",length(grep("novel",RyhB.regulon[,3])))))
#Plot the inferred GcvB regulon when CopraRNA predictions with p-value <= 0.01 were used as priors (Fig 4D)
#Load the output files of the corresponding Inferelator run
load("../Inferelator_output_files/Ecoli_CopraRNA_sRNA_priors/gcvB_silico_1_BBSR_1.1_100_0/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_CopraRNA_sRNA_priors/gcvB_silico_1_BBSR_1.1_100_0/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_CopraRNA_sRNA_priors/gcvB_silico_1_BBSR_1.1_100_0/params_and_input.RData")
#Create final network model
source("../Miscellaneous_scripts/create_final_networks.R")
#Save the inferred GcvB regulon
GcvB.regulon<-create.network("gcvB_b4443_12",recovered.interactions.matrix,novel.interactions.matrix)
GcvB.regulon.igraph.format<-graph_from_data_frame(GcvB.regulon[,1:2], union(GcvB.regulon[,"Regulator"],GcvB.regulon[,"Target"]), directed = T)
plot(GcvB.regulon.igraph.format,layout = layout_nicely(GcvB.regulon.igraph.format), edge.arrow.size =0.4,vertex.label.cex=0.8,vertex.size=21,main="Fig 4D",vertex.shape=c("square",rep("circle",nrow(GcvB.regulon))),vertex.label.color=c("blue",rep("black",length(grep("prior",GcvB.regulon[,3]))),rep("white",length(grep("novel",GcvB.regulon[,3])))))
#Plot the inferred Spot 42 regulon when CopraRNA predictions with p-value <= 0.01 & associated with enriched functional terms were used as priors (Fig 4E)
#Load the output files of the corresponding Inferelator run
load("../Inferelator_output_files/Ecoli_CopraRNA_sRNA_priors/spf_silico_3_BBSR_1.1_100_0/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_CopraRNA_sRNA_priors/spf_silico_3_BBSR_1.1_100_0/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_CopraRNA_sRNA_priors/spf_silico_3_BBSR_1.1_100_0/params_and_input.RData")
#Create final network model
source("../Miscellaneous_scripts/create_final_networks.R")
#Save the inferred Spot 42 regulon
Spot42.regulon<-create.network("spf_b3864_15",recovered.interactions.matrix,novel.interactions.matrix)
Spot42.regulon.igraph.format<-graph_from_data_frame(Spot42.regulon[,1:2], union(Spot42.regulon[,"Regulator"],Spot42.regulon[,"Target"]), directed = T)
plot(Spot42.regulon.igraph.format,layout = layout_nicely(Spot42.regulon.igraph.format), edge.arrow.size =0.4,vertex.label.cex=0.8,vertex.size=30,main="Fig 4E",vertex.shape=c("square",rep("circle",nrow(Spot42.regulon))),vertex.label.color=c("blue",rep("black",length(grep("prior",Spot42.regulon[,3]))),rep("white",length(grep("novel",Spot42.regulon[,3])))))
```
4. Create sRNA regulons shown in Fig 5. This figure was created in Cytoscape. Here we use the igraph package to get raw drafts.
```{r}
#Load the output files of the Inferelator run for E. coli
load("../Inferelator_output_files/Ecoli_8sRNAs/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/Ecoli_8sRNAs/params_and_input.RData")
#Create final network model
source("../Miscellaneous_scripts/create_final_networks.R")
#Create table with sRNA-mRNA interactions for all eight sRNAs
sRNAs<-colnames(IN$priors.mat)[140:147]
sRNAs.network<-create.network(sRNAs,recovered.interactions.matrix,novel.interactions.matrix)
#Plot the inferred Spot 42 regulon
Spot42.regulon<-sRNAs.network[grep("spf",sRNAs.network[,1]),]
Spot42.regulon.igraph.format<-graph_from_data_frame(Spot42.regulon[,1:2], union(Spot42.regulon[,"Regulator"],Spot42.regulon[,"Target"]), directed = T)
plot(Spot42.regulon.igraph.format,layout = layout_nicely(Spot42.regulon.igraph.format), edge.arrow.size =0.4,vertex.label.cex=1,vertex.size=30,main="Fig 5A",vertex.shape=c("square",rep("circle",nrow(Spot42.regulon))),vertex.label.color=c("blue",rep("black",length(grep("prior",Spot42.regulon[,3]))),rep("white",length(grep("novel",Spot42.regulon[,3])))))
#Plot the inferred GcvB regulon
GcvB.regulon<-sRNAs.network[grep("gcvB",sRNAs.network[,1]),]
GcvB.regulon.igraph.format<-graph_from_data_frame(GcvB.regulon[,1:2], union(GcvB.regulon[,"Regulator"],GcvB.regulon[,"Target"]), directed = T)
plot(GcvB.regulon.igraph.format,layout = layout_nicely(GcvB.regulon.igraph.format), edge.arrow.size =0.4,vertex.label.cex=1,vertex.size=30,main="Fig 5B",vertex.shape=c("square",rep("circle",nrow(GcvB.regulon))),vertex.label.color=c("blue",rep("black",length(grep("prior",GcvB.regulon[,3]))),rep("white",length(grep("novel",GcvB.regulon[,3])))))
#Plot the inferred PrrF regulon in P. aeruginosa
#Load the output files of the Inferelator run for P. aeruginosa
load("../Inferelator_output_files/PrrF_Paeruginosa/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/PrrF_Paeruginosa/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/PrrF_Paeruginosa/params_and_input.RData")
#Create final network model
source("../Miscellaneous_scripts/create_final_networks.R")
#Create table with PrrF-mRNA interactions 
PrrF.regulon<-create.network("PrrF",recovered.interactions.matrix,novel.interactions.matrix)
PrrF.regulon.igraph.format<-graph_from_data_frame(PrrF.regulon[,1:2], union(PrrF.regulon[,"Regulator"],PrrF.regulon[,"Target"]), directed = T)
plot(PrrF.regulon.igraph.format,layout = layout_nicely(PrrF.regulon.igraph.format), edge.arrow.size =0.4,vertex.label.cex=0.65,vertex.size=30,main="Fig 5C",vertex.shape=c("square",rep("circle",nrow(PrrF.regulon))),vertex.label.color=c("blue",rep("black",length(grep("prior",PrrF.regulon[,3]))),rep("white",length(grep("novel",PrrF.regulon[,3])))))
#Plot the inferred FsrA regulon in B. subtilis
#Load the output files of the Inferelator run for B. subtilis
load("../Inferelator_output_files/FsrA_Bsubtilis/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/FsrA_Bsubtilis/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/FsrA_Bsubtilis/params_and_input.RData")
#Create final network model
source("../Miscellaneous_scripts/create_final_networks.R")
#Create table with PrrF-mRNA interactions 
FsrA.regulon<-create.network(FsrA,recovered.interactions.matrix,novel.interactions.matrix)
#Replace locus ID with common sRNA name
FsrA.regulon[,1]<-"FsrA"
FsrA.regulon.igraph.format<-graph_from_data_frame(FsrA.regulon[,1:2], union(FsrA.regulon[,"Regulator"],FsrA.regulon[,"Target"]), directed = T)
plot(FsrA.regulon.igraph.format,layout = layout_nicely(FsrA.regulon.igraph.format), edge.arrow.size =0.4,vertex.label.cex=0.55,vertex.size=32,main="Fig 5D",vertex.shape=c("square",rep("circle",nrow(FsrA.regulon))),vertex.label.color=c("blue",rep("black",length(grep("prior",FsrA.regulon[,3]))),rep("white",length(grep("novel",FsrA.regulon[,3])))))
#Plot the inferred RsaE regulon in S. aureus
#Load the output files of the Inferelator run for S. aures
load("../Inferelator_output_files/RsaE_Saureus/betas_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/RsaE_Saureus/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData")
load("../Inferelator_output_files/RsaE_Saureus/params_and_input.RData")
#Create final network model
source("../Miscellaneous_scripts/create_final_networks.R")
#Create table with RsaE-mRNA interactions 
RsaE.recovered.targets<-names(which(recovered.interactions.matrix[,RsaE]!=0))
RsaE.recovered.targets<-sapply(1:length(RsaE.recovered.targets), function(x){gsub("SAOUHSC","SA",RsaE.recovered.targets[x])})
RsaE.novel.targets<-names(which(novel.interactions.matrix[,RsaE]!=0))
RsaE.novel.targets<-sapply(1:length(RsaE.novel.targets), function(x){gsub("SAOUHSC","SA",RsaE.novel.targets[x])})
RsaE.regulon<-rbind(cbind(rep("RsaE",length(RsaE.recovered.targets)),RsaE.recovered.targets,rep("priors",length(RsaE.recovered.targets))),cbind(rep("RsaE",length(RsaE.novel.targets)),RsaE.novel.targets,rep("novel",length(RsaE.novel.targets))))
colnames(RsaE.regulon)<-c("Regulator","Target","Interaction")
RsaE.regulon.igraph.format<-graph_from_data_frame(RsaE.regulon[,1:2], union(RsaE.regulon[,"Regulator"],RsaE.regulon[,"Target"]), directed = T)
plot(RsaE.regulon.igraph.format,layout = layout_nicely(RsaE.regulon.igraph.format), edge.arrow.size =0.4,vertex.label.cex=0.55,vertex.size=32,main="Fig 5E",vertex.shape=c("square",rep("circle",nrow(RsaE.regulon))),vertex.label.color=c("blue",rep("black",length(grep("prior",RsaE.regulon[,3]))),rep("white",length(grep("novel",RsaE.regulon[,3])))))
```


